A new experimental application of least-squares techniques for the estimation of the induction motor parameters

This paper deals with a new experimental approach to the parameter estimation of induction motors with Least-Squares techniques. In particular, it exploits the robustness of Total Least-Squares (TLS) techniques in noisy environments by using a new neuron, the TLS EXIN, which is easily implemented on-line. After showing that Ordinary Least-Squares (OLS) algorithms, classically employed in literature, are quite unreliable in presence of noisy measurements, which is not the case for TLS, the TLS EXIN neuron is:applied numerically and experimentally for retrieving the parameters of the induction motor by means of a test-bench. Additionally, for the case of very noisy data, a refinement of the TLS estimation has been obtained by the application of a constrained optimisation algorithm which explicitly takes into account the relationships among the K-parameters. The strength of this approach and the enhancement obtained is fully demonstrated first numerically and then verified experimentally.